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Differentially private contrastive learning no longer needs to sacrifice so much accuracy, thanks to a new method that cleverly bounds gradient dependencies.
Discrete diffusion models can be sped up by 14x by intelligently choosing which tokens to sample at each step, without sacrificing accuracy.
